Load forecasting in a utility grid is an integral part of energy management. Growing penetration of distributed energy resources such as solar photovoltaic (“PV”) power plants, wind power plants, and power storage plants has changed conventional utility practices for generating, transmitting, and distributing electric power.
A group of power plants, electrical energy consumption devices, and associated infrastructure spread over a geographical area may be referred to as a utility grid. Variations in power generation by distributed energy resources due to their intermittent nature and related changes in energy consumption can cause variations in operating conditions in a utility grid, such as voltage and frequency, beyond their standard or desired ranges.
Typically, operating conditions in a utility grid are managed by an energy management system (“EMS”). The utility grid's energy management system maintains the balance between all available energy resources including centralized energy resources such as coal, natural gas, oil or hydro and distributed energy resources such as solar, wind or storage, and energy uses such as uncontrolled and controlled electrical energy consumption devices. Load forecasting is used by the energy management system to reduce the imbalance between the available energy resources and intended uses thereby maintaining operating conditions in the utility grid within their standard or desired ranges.
Typically, an electrical energy consumption device or load in a utility grid is characterized by a load curve or profile. A load profile is a plot of electrical energy consumption variation as a function of time for a load or group of loads. Daily, weekly, monthly, and yearly load profiles are commonly developed and used in order to gain insight into the usage behavior of a group of loads. Loads in a utility grid may be classified as one of two major types, namely, conforming loads and non-conforming loads. A conforming load, relative to a group of loads, has a load profile that looks similar to the group's load profile. A non-conforming curve does not.
Due to their conventional behavioral patterns, electrical energy consumption for conforming loads may be forecast with a high level of accuracy using statistical forecasting methods. In contrast, electrical energy consumption for nonconforming loads is typically forecast with lower accuracy. If the load profile of nonconforming loads are unknown, their presence in a utility grid presents a significant challenge for the energy management system.
Growing penetration of distributed energy resources may change the balance between centralized energy resources and energy consumption in utility grids. To maintain this balance, the grid may be characterized by an equivalent load referred to as a “net load”. For a portion of a utility grid, the net load represents the difference between the power demand of a group of loads and the power generated by distributed energy resources located within that portion of the utility grid.
As distributed energy resources are typically intermittent by their nature, their presence in a portion of the utility grid makes the net load in that portion of the grid non-conforming. This creates a significant load forecasting and energy management problem and reduces the energy management system's ability to maintain the grid's stability.
A need therefore exists for an improved utility grid, net load forecasting system, and method for forecasting net load in a utility grid having distributed energy resources. Accordingly, a solution that addresses, at least in part, the above and other shortcomings is desired.